A Practical Guide for Economists
The Ohio State University
Part I
AI in Your Research
For the following use cases, discuss whether you think AI is appropriate to use as part of your research:
Let’s Get More Specific!
You use AI to rewrite paragraphs you’ve already written to make them sound more professional and improve grammar.
You ask AI to write code to merge two datasets and run a standard regression based on your natural language instructions. You check the code and it seems correct.
You ask AI to “summarize the top 10 most influential papers on [X].” You use this summary to develop your literature review in a paper.
You ask AI to propose three novel research questions based on the gap in the literature between Paper X and Paper Y. You choose one and it becomes the basis for your paper for your dissertation.
Your advisor asks you to review a paper for the AJAE. You upload the paper to AI and ask it to review the paper. You use these as the primary critiques of the paper.
Part II
My Thoughts on AI
AI has made it virtually costless to write grammatically correct prose with no spelling errors.
AI has given everyone a (near) Ph.D.-level coding assistant and data analyst.
What is the equilibrium response for economics job market candidates?
The average quality (and expectations) of a JMP will be higher every year
Expectations for more complex analyses will increase
Bad writing will likely become a very bad signal
Boring/generic writing will signal overreliance on AI
Interpersonal skills will become more critical
1. Learn the fundamentals — You can’t evaluate code you don’t understand. AI will confidently give you wrong answers—you need to catch them. Think of AI as a fast RA who occasionally hallucinates.
2. But don’t fall behind — Your peers are using these tools. The productivity gap compounds quickly. “I prefer the old way” is not a competitive strategy.
3. The old system was imperfect — Research errors happened before AI—and were caught late or not at all. Better workflows + AI review can catch errors earlier.
The goal isn’t “AI vs. no AI”—it’s building systems that make research more robust, better, and more replicable.
Part III
Building Your Stack
Work from OneDrive, Dropbox, or Google Drive—not your local machine alone.
A consistent folder structure makes AI tools more effective because they can understand your project organization.
project-folder/
├── data/
│ ├── raw/
│ └── clean/
├── code/
│ ├── 01-clean.R
│ ├── 02-analysis.R
│ └── 03-figures.R
├── output/
├── paper/
└── README.md
No more analysis_v3_final_FINAL2.R — Git tracks it all.
- model <- lm(y ~ x1 + x2, data = df)
+ model <- lm(y ~ x1 + x2 + x1:x2, data = df) # added interactionConfused on how to set up Git and GitHub? Ask AI!
What: Terminal-based, agentic coding assistant
Best for: Data analysis workflows, file operations & project setup, autonomous multi-step tasks, Git integration
Runs from command line
What: AI-powered code editor
Best for: Iterative code editing, larger software projects, inline suggestions while typing, tab completion on steroids
Overleaf = collaborative LaTeX editing in the browser
GitHub sync = version control for your paper
The workflow:
Code → Figures & Tables → GitHub Repo → Overleaf Paper
One integrated system: Code changes automatically flow through to your paper.
AI will make errors and add analyses/code you didn’t ask it to — It will confidently change code that generates plausible-looking results but be completely wrong.
Don’t let AI do your economic thinking — It’s a tool, not a co-author. Identification, intuition, and interpretation are yours.
Always test AI-generated code — Run it. Check edge cases. Verify results make sense.
Be thoughtful about sensitive data — Know what data you’re sending to AI services.
| Resource | Description |
|---|---|
| Kevin Bryan’s “Tech Stack” | Comprehensive guide to modern research tools for economists |
| Fernández-Villaverde’s Git Tutorial | Detailed Git tutorial for academic research |
| Claude Code Docs | Official documentation for Claude’s coding assistant |
| Cursor | AI-first code editor built on VS Code |
Part IV
Live Demonstration
Gabriel Lade
C. William Swank Chair in Rural-Urban Policy
Department of Agricultural, Environmental, and Development Economics
The Ohio State University